改进了早期作物分类,使用新的基于融合的机器学习方法与Sentinel-2A和Landsat 8-9数据进行了改进
Muhammad Daniyal Jamil1, Muhammad Zahid Abbas1, Muhammad Farhan Saeed2
1Department of Computer Science, COMSATS University Islamabad, Vehari Campus, Vehari, 61100, Pakistan.
Environmental monitoring and assessment
|August 6, 2025
概括
这项研究引入了一种新的深度学习方法,将光谱和纹理数据结合起来,用于早期作物分类. 这种方法通过融合卫星图像显著提高了准确性,有助于农业监测.
科学领域:
- 遥感 遥感 遥感 遥感
- 农业科学 农业科学
- 机器学习 机器学习
背景情况:
- 早期作物分类是很困难的,因为在作物类型中具有相似的光谱和纹理特征.
- 准确的作物识别对于有效的农业管理和监测至关重要.
研究的目的:
- 开发和评估一种基于融合的新型深度学习方法,用于增强早期作物分类.
- 用集成遥感数据提高作物识别的准确性和可靠性.
主要方法:
- 提出了基于融合的深度学习方法,整合了纹理和光谱特征.
- 陆地卫星8-9和卫星2A的数据是使用格兰-施密特方法合并的.
- 使用多补丁灰色水平共发生矩阵 (GLCM) 提取纹理特征,并通过光谱指数获得光谱特征 (EVI,NDVI).
- 训练和评估了包括深度神经网络 (DNN) 在内的五种机器学习模型.
主要成果:
- 提出的基于融合的深度学习方法表现出高性能.
- 深度神经网络 (DNN) 的准确度为0.89,精度为0.88,回忆率为0.91,F1得分为0.90.
- 融合的光谱和纹理数据的整合显著改善了分类结果.
结论:
- 基于融合的深度学习方法有效地提高了早期作物分类的准确性.
- 结合多种数据源和先进的机器学习技术,为农业遥感挑战提供了一个有希望的解决方案.
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